Priority Budget Hiring Plan

Hard

An operations team is building a deterministic hiring engine for a fixed monthly payroll expansion budget. Table: Candidates employee_id: unique candidate identifier experience: candidate level category ("Senior" or "Junior") salary: monthly salary expectation (unique across all candidates) Budget policy: Initial budget is 70000. Phase 1: hire seniors in strictly increasing salary order until the next senior is unaffordable. Phase 2: with the remaining budget, hire juniors in strictly increasing salary order until the next junior is unaffordable. Task: Return the employee IDs of every hired candidate under this two-phase policy. Output requirements: Return exactly one column: employee_id Each employee_id should appear at most once Output order may be arbitrary (deterministic ordering is recommended) Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 1           | Junior     | 10000  |
| 9           | Junior     | 15000  |
| 2           | Senior     | 20000  |
| 11          | Senior     | 16000  |
| 13          | Senior     | 50000  |
| 4           | Junior     | 40000  |

Output

[
  {"employee_id":1},
  {"employee_id":2},
  {"employee_id":9},
  {"employee_id":11}
]

Explanation

Seniors are considered by salary: 16000 (id 11), 20000 (id 2), 50000 (id 13). The first two are hired (36000 total), but id 13 is unaffordable. Remaining budget is 34000, so juniors 10000 (id 1) and 15000 (id 9) are hired, while 40000 (id 4) is not.

Example 2

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 1           | Junior     | 25000  |
| 9           | Junior     | 10000  |
| 2           | Senior     | 85000  |
| 11          | Senior     | 80000  |
| 13          | Senior     | 90000  |
| 4           | Junior     | 30000  |

Output

[
  {"employee_id":1},
  {"employee_id":4},
  {"employee_id":9}
]

Explanation

No senior can be hired because the cheapest senior salary exceeds 70000. The full budget is then used for juniors in salary order: 10000, 25000, and 30000 are all affordable.

Example 3

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 101         | Senior     | 30000  |
| 102         | Senior     | 40000  |
| 103         | Junior     | 5000   |
| 104         | Junior     | 6000   |

Output

[
  {"employee_id":101},
  {"employee_id":102}
]

Explanation

Hiring seniors 30000 and 40000 consumes the entire 70000 budget, leaving nothing for juniors.

Constraints

0 <= number of rows in Candidates <= 200000
1 <= employee_id <= 1000000000
experience is either Senior or Junior
1 <= salary <= 1000000000
employee_id values are unique
salary values are unique
Hiring order inside each phase is ascending salary
A phase stops when the next candidate in that phase is unaffordable
Juniors are considered only after the senior phase is complete
Return exactly one column: employee_id
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with one column employee_id

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Priority Budget Hiring Plan

Hard

An operations team is building a deterministic hiring engine for a fixed monthly payroll expansion budget. Table: Candidates employee_id: unique candidate identifier experience: candidate level category ("Senior" or "Junior") salary: monthly salary expectation (unique across all candidates) Budget policy: Initial budget is 70000. Phase 1: hire seniors in strictly increasing salary order until the next senior is unaffordable. Phase 2: with the remaining budget, hire juniors in strictly increasing salary order until the next junior is unaffordable. Task: Return the employee IDs of every hired candidate under this two-phase policy. Output requirements: Return exactly one column: employee_id Each employee_id should appear at most once Output order may be arbitrary (deterministic ordering is recommended) Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 1           | Junior     | 10000  |
| 9           | Junior     | 15000  |
| 2           | Senior     | 20000  |
| 11          | Senior     | 16000  |
| 13          | Senior     | 50000  |
| 4           | Junior     | 40000  |

Output

[
  {"employee_id":1},
  {"employee_id":2},
  {"employee_id":9},
  {"employee_id":11}
]

Explanation

Example 2

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 1           | Junior     | 25000  |
| 9           | Junior     | 10000  |
| 2           | Senior     | 85000  |
| 11          | Senior     | 80000  |
| 13          | Senior     | 90000  |
| 4           | Junior     | 30000  |

Output

[
  {"employee_id":1},
  {"employee_id":4},
  {"employee_id":9}
]

Explanation

No senior can be hired because the cheapest senior salary exceeds 70000. The full budget is then used for juniors in salary order: 10000, 25000, and 30000 are all affordable.

Example 3

Input

Candidates:
| employee_id | experience | salary |
|-------------|------------|--------|
| 101         | Senior     | 30000  |
| 102         | Senior     | 40000  |
| 103         | Junior     | 5000   |
| 104         | Junior     | 6000   |

Output

[
  {"employee_id":101},
  {"employee_id":102}
]

Explanation

Hiring seniors 30000 and 40000 consumes the entire 70000 budget, leaving nothing for juniors.

Constraints

0 <= number of rows in Candidates <= 200000
1 <= employee_id <= 1000000000
experience is either Senior or Junior
1 <= salary <= 1000000000
employee_id values are unique
salary values are unique
Hiring order inside each phase is ascending salary
A phase stops when the next candidate in that phase is unaffordable
Juniors are considered only after the senior phase is complete
Return exactly one column: employee_id
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with one column employee_id